Ajil Jalal

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PhD Student
Department of Electrical Engineering
The University of Texas at Austin
Google Scholar Page
GitHub page
ajiljalal@utexas.edu

News

  • September 2021: Our paper on posterior sampling for MRI has been accepted at NeurIPS 2021. We show that posterior sampling with score-based generative models is competitive with state-of-the-art deep learning methods. We also show that our technique is robust to anatomy shift and shift in MRI sampling patterns. Theoretically, we show that posterior sampling with Gaussian measurements is extremely robust to distribution shift, while posterior sampling with arbitrary measurements is competitive if there's no distribution shift.

  • May 2021: Three new papers at ICML 2021. We prove that Posterior Sampling is instance-optimal for compressed sensing and we propose new definitions for fairness in generative processes. Code and models are available here: link to code.

  • October 2020: Our paper has been selected for an oral presentation at the NeurIPS 2020 Workshop on Deep Learning and Inverse Problems, Vancouver, Canada. We show that conditional sampling is provably good for solving linear problems using full dimensional generative models.

  • October 2020: Our paper shows how generative models can give performance improvements in channel estimation, and will appear in the IEEE JSAC Series on Machine Learning for Communications and Networks.

  • September 2020: Our paper has been accepted as a poster presentation at NeurIPS 2020, Vancouver, Canada. We develop a new robust algorithm for compressed sensing using generative models.

  • May 2020: New survey paper on deep learning techniques for inverse problems in imaging. We introduced a new taxonomy on supervised versus unsupervised methods.

  • January 2020: Invited talk at IIT Madras.

  • December 2019: I organized the Deep Learning and Inverse Problems social at NeurIPS 2019, Vancouver, Canada! I generated $1000 in funding for this social.

  • November 2019: Invited talk at Asilomar Conference on Signals, Systems, and Computers, in California, USA.

About Me

I am a final year PhD student at UT Austin. I have spent five wonderful years advised by Prof. Alex Dimakis, and have been lucky to collaborate with Prof. Eric Price on several projects. I received my B.Tech. (Honors) in Electrical Engineering from IIT Madras in 2016, where I worked closely with Prof. Rahul Vaze, Prof. Umang Bhaskar, and Prof. Krishna Jagannathan.

My research explores the theory and practice of algorithms that use deep generative models for solving signal processing problems. On the theoretical side, my work has produced algorithms that are provably robust and achieve optimal statistical complexities. On the practical side, our algorithms are competitive with state-of-the-art deep learning methods on fastMRI data, with the added benefits of flexibility and modularity.